Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries.

Chowdhury, Rajiv; Heng, Kevin; Shawon, Md Shajedur Rahman; Goh, Gabriel; Okonofua, Daisy; Ochoa-Rosales, Carolina; Gonzalez-Jaramillo, Valentina; Bhuiya, Abbas; Reidpath, Daniel; Prathapan, Shamini; Shahzad, Sara; Althaus, Christian L.; Gonzalez-Jaramillo, Nathalia; Franco, Oscar H. (2020). Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries. European journal of epidemiology, 35(5), pp. 389-399. Springer 10.1007/s10654-020-00649-w

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To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs-increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal "break" when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R0) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a "schedule" of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
10 Strategic Research Centers > Center for Space and Habitability (CSH)

UniBE Contributor:

Heng, Kevin; González Jaramillo, Valentina; Althaus, Christian; Gonzalez Jaramillo, Nathalia and Franco Duran, Oscar Horacio

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 520 Astronomy

ISSN:

0393-2990

Publisher:

Springer

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

27 May 2020 12:05

Last Modified:

25 Jun 2020 14:38

Publisher DOI:

10.1007/s10654-020-00649-w

PubMed ID:

32430840

Uncontrolled Keywords:

COVID-19 Dynamic interventions Epidemiology Infectious disease Prediction modelling

BORIS DOI:

10.7892/boris.144321

URI:

https://boris.unibe.ch/id/eprint/144321

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